{"id":"https://openalex.org/W3080918539","doi":"https://doi.org/10.1145/3394486.3403269","title":"Stable Learning via Differentiated Variable Decorrelation","display_name":"Stable Learning via Differentiated Variable Decorrelation","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3080918539","doi":"https://doi.org/10.1145/3394486.3403269","mag":"3080918539"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403269","doi":"https://doi.org/10.1145/3394486.3403269","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403269","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112662608","display_name":"Zheyan Shen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheyan Shen","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009228005","display_name":"Peng Cui","orcid":"https://orcid.org/0000-0003-2957-8511","observed_orcids":["https://orcid.org/0000-0003-2957-8511"]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Cui","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004072676","display_name":"Jiashuo Liu","orcid":"https://orcid.org/0000-0002-9159-1752","observed_orcids":["https://orcid.org/0000-0002-9159-1752"]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiashuo Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100378792","display_name":"Tong Zhang","orcid":"https://orcid.org/0000-0002-5511-2558","observed_orcids":["https://orcid.org/0000-0002-5511-2558","https://orcid.org/0000-0001-5818-4285","https://orcid.org/0000-0002-7025-6365","https://orcid.org/0000-0002-8838-4963","https://orcid.org/0000-0001-6212-4891","https://orcid.org/0000-0002-3506-0180","https://orcid.org/0000-0002-6807-0641","https://orcid.org/0000-0003-1148-4322","https://orcid.org/0000-0002-0131-0129","https://orcid.org/0000-0001-8163-3050","https://orcid.org/0000-0002-3481-6062"]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Tong Zhang","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bo Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059324399","display_name":"Zhitang Chen","orcid":"https://orcid.org/0000-0001-7197-4601","observed_orcids":["https://orcid.org/0000-0001-7197-4601","https://orcid.org/0000-0002-4211-997X"]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhitang Chen","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shen Zhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shen Zhen, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8714,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.89023057,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2185","last_page":"2193"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2800000011920929,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2800000011920929,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.1404000073671341,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.06400000303983688,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sample-weighting","display_name":"sample weighting","score":0.45899999141693115},{"id":"https://openalex.org/keywords/variable-clustering","display_name":"variable clustering","score":0.7860000133514404},{"id":"https://openalex.org/keywords/robustness","display_name":"robustness","score":0.4009999930858612},{"id":"https://openalex.org/keywords/model-stability","display_name":"model stability","score":0.5230000019073486},{"id":"https://openalex.org/keywords/variance-inflation","display_name":"variance inflation","score":0.6710000038146973}],"concepts":[{"id":"https://openalex.org/C177860922","wikidata":"https://www.wikidata.org/wiki/Q788608","display_name":"Decorrelation","level":2,"score":0.8678494691848755},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6860082149505615},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6406981945037842},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.631947934627533},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6266058087348938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5283889174461365},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5120554566383362},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4891100525856018},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36381661891937256},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.361721396446228},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.262258917093277},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3394486.3403269","doi":"https://doi.org/10.1145/3394486.3403269","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403269","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-105941","doi":null,"is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-105941","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"},{"id":"pmh:oai:repository.ust.hk:1783.1-105941","doi":null,"is_oa":false,"landing_page_url":"http://repository.ust.hk/ir/Record/1783.1-105941","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1882958252","https://openalex.org/W2034368206","https://openalex.org/W2066124661","https://openalex.org/W2092114106","https://openalex.org/W2104068492","https://openalex.org/W2104094955","https://openalex.org/W2112483442","https://openalex.org/W2122825543","https://openalex.org/W2127218421","https://openalex.org/W2135046866","https://openalex.org/W2155858138","https://openalex.org/W2158754421","https://openalex.org/W2159291411","https://openalex.org/W2165698076","https://openalex.org/W2170612786","https://openalex.org/W2271361270","https://openalex.org/W2299467264","https://openalex.org/W2565113373","https://openalex.org/W2599025709","https://openalex.org/W2742797692","https://openalex.org/W2763549966","https://openalex.org/W2790376986","https://openalex.org/W2807992610","https://openalex.org/W2894881080","https://openalex.org/W2903585907","https://openalex.org/W2997591099","https://openalex.org/W2997746169","https://openalex.org/W3016824580","https://openalex.org/W3019502473","https://openalex.org/W4234314512","https://openalex.org/W4290473204"],"related_works":["https://openalex.org/W2997591099","https://openalex.org/W2997746169","https://openalex.org/W4312695313","https://openalex.org/W4382202905","https://openalex.org/W3163277370","https://openalex.org/W4403977050","https://openalex.org/W3090730144","https://openalex.org/W3005141333","https://openalex.org/W4226303981","https://openalex.org/W4281911535"],"abstract_inverted_index":{"Recently,":[0],"as":[1,14,87],"the":[2,33,42,80,118,122,140,144,151,162,169,183,188,194,200,212,216,232,240,245],"applications":[3],"of":[4,22,35,66,99,127,185,196,207,218,234],"artificial":[5],"intelligence":[6],"gradually":[7],"seeping":[8],"into":[9,168],"some":[10],"risk-sensitive":[11],"areas":[12],"such":[13,91,135],"justice,":[15],"healthcare":[16],"and":[17,28,59,83,102,154,173,199,226,244],"autonomous":[18],"driving,":[19],"an":[20],"upsurge":[21],"research":[23],"interest":[24],"on":[25,182,223,238],"model":[26,52,82,100,241],"stability":[27,104,195,247],"robustness":[29],"has":[30],"arisen":[31],"in":[32,69],"field":[34],"machine":[36],"learning.":[37],"Rather":[38],"than":[39],"purely":[40],"fitting":[41],"observed":[43],"training":[44],"data,":[45],"stable":[46,67],"learning":[47,68],"tries":[48],"to":[49,112,116,150,193,210],"learn":[50],"a":[51,88,96,113,125,175,205],"with":[53],"uniformly":[54],"good":[55],"performance":[56],"under":[57],"non-stationary":[58],"agnostic":[60],"testing":[61],"data.":[62],"The":[63],"key":[64],"challenge":[65],"practice":[70],"is":[71],"that":[72,134],"we":[73,93,132,160],"do":[74],"not":[75],"have":[76],"any":[77],"knowledge":[78],"about":[79],"true":[81],"test":[84],"data":[85,164],"distribution":[86],"priori.":[89],"Under":[90],"condition,":[92],"cannot":[94],"expect":[95],"faithful":[97],"estimation":[98,243],"parameters":[101],"its":[103],"over":[105,248],"wild":[106],"changing":[107,249],"environments.":[108],"Previous":[109],"methods":[110],"resort":[111],"reweighting":[114],"scheme":[115],"remove":[117,211],"correlations":[119,198,213],"between":[120,138,215],"all":[121,139],"variables":[123,141,189,217],"through":[124],"set":[126,206],"new":[128],"sample":[129,146,208],"weights.":[130],"However,":[131],"argue":[133],"aggressive":[136],"decorrelation":[137,171,202],"may":[142],"cause":[143],"over-reduced":[145],"size,":[147],"which":[148],"leads":[149],"variance":[152],"inflation":[153],"possible":[155],"underperformance.":[156],"In":[157],"this":[158],"paper,":[159],"incorporate":[161],"unlabled":[163],"from":[165],"multiple":[166],"environments":[167],"variable":[170,201],"framework":[172],"propose":[174],"Differentiated":[176],"Variable":[177],"Decorrelation":[178],"(DVD)":[179],"algorithm":[180,237],"based":[181],"clustering":[184],"variables.":[186],"Specifically,":[187],"are":[190],"clustered":[191],"according":[192],"their":[197],"module":[203],"learns":[204],"weights":[209],"merely":[214],"different":[219],"clusters.":[220],"Empirical":[221],"studies":[222],"both":[224],"synthetic":[225],"real":[227],"world":[228],"datasets":[229],"clearly":[230],"demonstrate":[231],"efficacy":[233],"our":[235],"DVD":[236],"improving":[239],"parameter":[242],"prediction":[246],"distributions.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2020-09-01T00:00:00"}
